On the classification and quantification of crystal defects after energetic bombardment by machine learned molecular dynamics simulations

The analysis of the damage on plasma facing materials (PFM), due to its direct interaction with the plasma environment, is needed to build the next generation of nuclear machines, where tungsten has been proposed as a candidate. In this work, we perform molecular dynamics (MD) simulations using a machine learned inter-atomic potential, based on the Gaussian Approximation Potential framework, to model better neutron bombardment mechanisms in pristine W lattices. The MD potential is trained to reproduce realistic short-range dynamics, the liquid phase, and the material recrystallization, which are important for collision cascades. The formation of point defects is quantified and classified by a descriptor vector (DV) based method, which is independent of the sample temperature and its constituents, requiring only modest computational resources. The locations of vacancies are calculated by the k-d-tree algorithm. The analysis of the damage in the W samples is compared to results obtained by EAM Finnis-Sinclair and Tersoff-ZBL potentials, at a sample temperature of 300 K and a primary knock-on atom (PKA) energy range of 0.5-10 keV, where a good agreement with the reported number of Frenkel pair is observed. Our results provide information about the advantages and limits of the machine learned MD simulations with respect to the standard ones. The formation of dumbbell and crowdion defects as a function of PKA is discussed.